ConvGeNBenchmarkResults/data_result/convGAN-old-5/imblearn_ozone_level.log

///////////////////////////////////////////
// Running convGAN on imblearn_ozone_level
///////////////////////////////////////////

Load 'data_input/imblearn_ozone_level'
from imblearn
Data loaded.
-> Shuffling data
### Start exercise for synthetic point generator

====== Step 1/5 =======
-> Shuffling data
-> Spliting data to slices

------ Step 1/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1912 synthetic samples
-> test with 'LR'
LR tn, fp: 434, 59
LR fn, tp: 2, 13
LR f1 score: 0.299
LR cohens kappa score: 0.263
LR average precision score: 0.379

-> test with 'GB'
GB tn, fp: 479, 14
GB fn, tp: 8, 7
GB f1 score: 0.389
GB cohens kappa score: 0.367

-> test with 'KNN'
KNN tn, fp: 408, 85
KNN fn, tp: 9, 6
KNN f1 score: 0.113
KNN cohens kappa score: 0.066


------ Step 1/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1912 synthetic samples
-> test with 'LR'
LR tn, fp: 435, 58
LR fn, tp: 4, 11
LR f1 score: 0.262
LR cohens kappa score: 0.224
LR average precision score: 0.210

-> test with 'GB'
GB tn, fp: 483, 10
GB fn, tp: 6, 9
GB f1 score: 0.529
GB cohens kappa score: 0.513

-> test with 'KNN'
KNN tn, fp: 399, 94
KNN fn, tp: 7, 8
KNN f1 score: 0.137
KNN cohens kappa score: 0.090


------ Step 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1912 synthetic samples
-> test with 'LR'
LR tn, fp: 438, 55
LR fn, tp: 6, 9
LR f1 score: 0.228
LR cohens kappa score: 0.189
LR average precision score: 0.125

-> test with 'GB'
GB tn, fp: 473, 20
GB fn, tp: 8, 7
GB f1 score: 0.333
GB cohens kappa score: 0.307

-> test with 'KNN'
KNN tn, fp: 409, 84
KNN fn, tp: 10, 5
KNN f1 score: 0.096
KNN cohens kappa score: 0.048


------ Step 1/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1912 synthetic samples
-> test with 'LR'
LR tn, fp: 428, 65
LR fn, tp: 5, 10
LR f1 score: 0.222
LR cohens kappa score: 0.182
LR average precision score: 0.218

-> test with 'GB'
GB tn, fp: 476, 17
GB fn, tp: 8, 7
GB f1 score: 0.359
GB cohens kappa score: 0.335

-> test with 'KNN'
KNN tn, fp: 420, 73
KNN fn, tp: 8, 7
KNN f1 score: 0.147
KNN cohens kappa score: 0.103


------ Step 1/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1912 synthetic samples
-> test with 'LR'
LR tn, fp: 430, 61
LR fn, tp: 3, 10
LR f1 score: 0.238
LR cohens kappa score: 0.203
LR average precision score: 0.180

-> test with 'GB'
GB tn, fp: 478, 13
GB fn, tp: 8, 5
GB f1 score: 0.323
GB cohens kappa score: 0.302

-> test with 'KNN'
KNN tn, fp: 388, 103
KNN fn, tp: 7, 6
KNN f1 score: 0.098
KNN cohens kappa score: 0.055


====== Step 2/5 =======
-> Shuffling data
-> Spliting data to slices

------ Step 2/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1912 synthetic samples
-> test with 'LR'
LR tn, fp: 425, 68
LR fn, tp: 5, 10
LR f1 score: 0.215
LR cohens kappa score: 0.174
LR average precision score: 0.191

-> test with 'GB'
GB tn, fp: 479, 14
GB fn, tp: 11, 4
GB f1 score: 0.242
GB cohens kappa score: 0.217

-> test with 'KNN'
KNN tn, fp: 399, 94
KNN fn, tp: 10, 5
KNN f1 score: 0.088
KNN cohens kappa score: 0.038


------ Step 2/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1912 synthetic samples
-> test with 'LR'
LR tn, fp: 444, 49
LR fn, tp: 5, 10
LR f1 score: 0.270
LR cohens kappa score: 0.234
LR average precision score: 0.210

-> test with 'GB'
GB tn, fp: 478, 15
GB fn, tp: 8, 7
GB f1 score: 0.378
GB cohens kappa score: 0.356

-> test with 'KNN'
KNN tn, fp: 398, 95
KNN fn, tp: 5, 10
KNN f1 score: 0.167
KNN cohens kappa score: 0.121


------ Step 2/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1912 synthetic samples
-> test with 'LR'
LR tn, fp: 432, 61
LR fn, tp: 1, 14
LR f1 score: 0.311
LR cohens kappa score: 0.275
LR average precision score: 0.487

-> test with 'GB'
GB tn, fp: 481, 12
GB fn, tp: 7, 8
GB f1 score: 0.457
GB cohens kappa score: 0.438

-> test with 'KNN'
KNN tn, fp: 385, 108
KNN fn, tp: 9, 6
KNN f1 score: 0.093
KNN cohens kappa score: 0.043


------ Step 2/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1912 synthetic samples
-> test with 'LR'
LR tn, fp: 436, 57
LR fn, tp: 5, 10
LR f1 score: 0.244
LR cohens kappa score: 0.206
LR average precision score: 0.153

-> test with 'GB'
GB tn, fp: 475, 18
GB fn, tp: 11, 4
GB f1 score: 0.216
GB cohens kappa score: 0.188

-> test with 'KNN'
KNN tn, fp: 437, 56
KNN fn, tp: 11, 4
KNN f1 score: 0.107
KNN cohens kappa score: 0.062


------ Step 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1912 synthetic samples
-> test with 'LR'
LR tn, fp: 421, 70
LR fn, tp: 3, 10
LR f1 score: 0.215
LR cohens kappa score: 0.179
LR average precision score: 0.202

-> test with 'GB'
GB tn, fp: 477, 14
GB fn, tp: 6, 7
GB f1 score: 0.412
GB cohens kappa score: 0.392

-> test with 'KNN'
KNN tn, fp: 432, 59
KNN fn, tp: 7, 6
KNN f1 score: 0.154
KNN cohens kappa score: 0.116


====== Step 3/5 =======
-> Shuffling data
-> Spliting data to slices

------ Step 3/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1912 synthetic samples
-> test with 'LR'
LR tn, fp: 427, 66
LR fn, tp: 3, 12
LR f1 score: 0.258
LR cohens kappa score: 0.219
LR average precision score: 0.311

-> test with 'GB'
GB tn, fp: 473, 20
GB fn, tp: 8, 7
GB f1 score: 0.333
GB cohens kappa score: 0.307

-> test with 'KNN'
KNN tn, fp: 415, 78
KNN fn, tp: 7, 8
KNN f1 score: 0.158
KNN cohens kappa score: 0.114


------ Step 3/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1912 synthetic samples
-> test with 'LR'
LR tn, fp: 438, 55
LR fn, tp: 4, 11
LR f1 score: 0.272
LR cohens kappa score: 0.235
LR average precision score: 0.147

-> test with 'GB'
GB tn, fp: 480, 13
GB fn, tp: 10, 5
GB f1 score: 0.303
GB cohens kappa score: 0.280

-> test with 'KNN'
KNN tn, fp: 413, 80
KNN fn, tp: 9, 6
KNN f1 score: 0.119
KNN cohens kappa score: 0.072


------ Step 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1912 synthetic samples
-> test with 'LR'
LR tn, fp: 444, 49
LR fn, tp: 4, 11
LR f1 score: 0.293
LR cohens kappa score: 0.258
LR average precision score: 0.177

-> test with 'GB'
GB tn, fp: 480, 13
GB fn, tp: 8, 7
GB f1 score: 0.400
GB cohens kappa score: 0.379

-> test with 'KNN'
KNN tn, fp: 416, 77
KNN fn, tp: 10, 5
KNN f1 score: 0.103
KNN cohens kappa score: 0.056


------ Step 3/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1912 synthetic samples
-> test with 'LR'
LR tn, fp: 430, 63
LR fn, tp: 5, 10
LR f1 score: 0.227
LR cohens kappa score: 0.187
LR average precision score: 0.167

-> test with 'GB'
GB tn, fp: 480, 13
GB fn, tp: 7, 8
GB f1 score: 0.444
GB cohens kappa score: 0.425

-> test with 'KNN'
KNN tn, fp: 423, 70
KNN fn, tp: 12, 3
KNN f1 score: 0.068
KNN cohens kappa score: 0.020


------ Step 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1912 synthetic samples
-> test with 'LR'
LR tn, fp: 425, 66
LR fn, tp: 2, 11
LR f1 score: 0.244
LR cohens kappa score: 0.210
LR average precision score: 0.379

-> test with 'GB'
GB tn, fp: 474, 17
GB fn, tp: 6, 7
GB f1 score: 0.378
GB cohens kappa score: 0.357

-> test with 'KNN'
KNN tn, fp: 364, 127
KNN fn, tp: 3, 10
KNN f1 score: 0.133
KNN cohens kappa score: 0.090


====== Step 4/5 =======
-> Shuffling data
-> Spliting data to slices

------ Step 4/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1912 synthetic samples
-> test with 'LR'
LR tn, fp: 418, 75
LR fn, tp: 3, 12
LR f1 score: 0.235
LR cohens kappa score: 0.195
LR average precision score: 0.296

-> test with 'GB'
GB tn, fp: 476, 17
GB fn, tp: 7, 8
GB f1 score: 0.400
GB cohens kappa score: 0.377

-> test with 'KNN'
KNN tn, fp: 419, 74
KNN fn, tp: 11, 4
KNN f1 score: 0.086
KNN cohens kappa score: 0.038


------ Step 4/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1912 synthetic samples
-> test with 'LR'
LR tn, fp: 437, 56
LR fn, tp: 2, 13
LR f1 score: 0.310
LR cohens kappa score: 0.274
LR average precision score: 0.225

-> test with 'GB'
GB tn, fp: 480, 13
GB fn, tp: 7, 8
GB f1 score: 0.444
GB cohens kappa score: 0.425

-> test with 'KNN'
KNN tn, fp: 427, 66
KNN fn, tp: 11, 4
KNN f1 score: 0.094
KNN cohens kappa score: 0.048


------ Step 4/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1912 synthetic samples
-> test with 'LR'
LR tn, fp: 441, 52
LR fn, tp: 3, 12
LR f1 score: 0.304
LR cohens kappa score: 0.269
LR average precision score: 0.204

-> test with 'GB'
GB tn, fp: 482, 11
GB fn, tp: 8, 7
GB f1 score: 0.424
GB cohens kappa score: 0.405

-> test with 'KNN'
KNN tn, fp: 396, 97
KNN fn, tp: 7, 8
KNN f1 score: 0.133
KNN cohens kappa score: 0.086


------ Step 4/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1912 synthetic samples
-> test with 'LR'
LR tn, fp: 424, 69
LR fn, tp: 3, 12
LR f1 score: 0.250
LR cohens kappa score: 0.211
LR average precision score: 0.272

-> test with 'GB'
GB tn, fp: 479, 14
GB fn, tp: 8, 7
GB f1 score: 0.389
GB cohens kappa score: 0.367

-> test with 'KNN'
KNN tn, fp: 393, 100
KNN fn, tp: 9, 6
KNN f1 score: 0.099
KNN cohens kappa score: 0.050


------ Step 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1912 synthetic samples
-> test with 'LR'
LR tn, fp: 423, 68
LR fn, tp: 3, 10
LR f1 score: 0.220
LR cohens kappa score: 0.184
LR average precision score: 0.224

-> test with 'GB'
GB tn, fp: 478, 13
GB fn, tp: 5, 8
GB f1 score: 0.471
GB cohens kappa score: 0.453

-> test with 'KNN'
KNN tn, fp: 395, 96
KNN fn, tp: 9, 4
KNN f1 score: 0.071
KNN cohens kappa score: 0.026


====== Step 5/5 =======
-> Shuffling data
-> Spliting data to slices

------ Step 5/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1912 synthetic samples
-> test with 'LR'
LR tn, fp: 444, 49
LR fn, tp: 3, 12
LR f1 score: 0.316
LR cohens kappa score: 0.282
LR average precision score: 0.262

-> test with 'GB'
GB tn, fp: 480, 13
GB fn, tp: 4, 11
GB f1 score: 0.564
GB cohens kappa score: 0.548

-> test with 'KNN'
KNN tn, fp: 369, 124
KNN fn, tp: 6, 9
KNN f1 score: 0.122
KNN cohens kappa score: 0.072


------ Step 5/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1912 synthetic samples
-> test with 'LR'
LR tn, fp: 424, 69
LR fn, tp: 3, 12
LR f1 score: 0.250
LR cohens kappa score: 0.211
LR average precision score: 0.139

-> test with 'GB'
GB tn, fp: 478, 15
GB fn, tp: 9, 6
GB f1 score: 0.333
GB cohens kappa score: 0.310

-> test with 'KNN'
KNN tn, fp: 404, 89
KNN fn, tp: 12, 3
KNN f1 score: 0.056
KNN cohens kappa score: 0.006


------ Step 5/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1912 synthetic samples
-> test with 'LR'
LR tn, fp: 449, 44
LR fn, tp: 6, 9
LR f1 score: 0.265
LR cohens kappa score: 0.229
LR average precision score: 0.177

-> test with 'GB'
GB tn, fp: 484, 9
GB fn, tp: 8, 7
GB f1 score: 0.452
GB cohens kappa score: 0.434

-> test with 'KNN'
KNN tn, fp: 426, 67
KNN fn, tp: 8, 7
KNN f1 score: 0.157
KNN cohens kappa score: 0.114


------ Step 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1912 synthetic samples
-> test with 'LR'
LR tn, fp: 427, 66
LR fn, tp: 3, 12
LR f1 score: 0.258
LR cohens kappa score: 0.219
LR average precision score: 0.238

-> test with 'GB'
GB tn, fp: 479, 14
GB fn, tp: 6, 9
GB f1 score: 0.474
GB cohens kappa score: 0.454

-> test with 'KNN'
KNN tn, fp: 429, 64
KNN fn, tp: 8, 7
KNN f1 score: 0.163
KNN cohens kappa score: 0.120


------ Step 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1912 synthetic samples
-> test with 'LR'
LR tn, fp: 422, 69
LR fn, tp: 3, 10
LR f1 score: 0.217
LR cohens kappa score: 0.181
LR average precision score: 0.338

-> test with 'GB'
GB tn, fp: 482, 9
GB fn, tp: 9, 4
GB f1 score: 0.308
GB cohens kappa score: 0.289

-> test with 'KNN'
KNN tn, fp: 418, 73
KNN fn, tp: 10, 3
KNN f1 score: 0.067
KNN cohens kappa score: 0.024

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 449, 75
LR fn, tp: 6, 14
LR f1 score: 0.316
LR cohens kappa score: 0.282
LR average precision score: 0.487


average:
LR tn, fp: 431.84, 60.76
LR fn, tp: 3.56, 11.04
LR f1 score: 0.257
LR cohens kappa score: 0.220
LR average precision score: 0.236


minimum:
LR tn, fp: 418, 44
LR fn, tp: 1, 9
LR f1 score: 0.215
LR cohens kappa score: 0.174
LR average precision score: 0.125


-----[ GB ]-----
maximum:
GB tn, fp: 484, 20
GB fn, tp: 11, 11
GB f1 score: 0.564
GB cohens kappa score: 0.548


average:
GB tn, fp: 478.56, 14.04
GB fn, tp: 7.64, 6.96
GB f1 score: 0.390
GB cohens kappa score: 0.369


minimum:
GB tn, fp: 473, 9
GB fn, tp: 4, 4
GB f1 score: 0.216
GB cohens kappa score: 0.188


-----[ KNN ]-----
maximum:
KNN tn, fp: 437, 127
KNN fn, tp: 12, 10
KNN f1 score: 0.167
KNN cohens kappa score: 0.121


average:
KNN tn, fp: 407.28, 85.32
KNN fn, tp: 8.6, 6.0
KNN f1 score: 0.113
KNN cohens kappa score: 0.067


minimum:
KNN tn, fp: 364, 56
KNN fn, tp: 3, 3
KNN f1 score: 0.056
KNN cohens kappa score: 0.006